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The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends. We trained a multitask long short-term memory model for total P (TP) using data from 430 rivers across the contiguous United States (CONUS). Trend analysis of reconstructed daily records (1980-2019) shows widespread decline in concentrations, with declining, increasing, and insignificantly changing trends in 60%, 28%, and 12% of the rivers, respectively. Concentrations in urban rivers have declined the most despite rising urban population in the past decades; concentrations in agricultural rivers however have mostly increased, suggesting not-as-effective controls of nonpoint sources in agriculture lands compared to point sources in cities. TP loss, calculated as fluxes by multiplying concentration and discharge, however exhibited an overall increasing rate of 6.5% per decade at the CONUS scale over the past 40 y, largely due to increasing river discharge. Results highlight the challenge of reducing TP loss that is complicated by changing river discharge in a warming climate., Competing Interests: Competing interests statement:The authors declare no competing interest.

Background: Spontaneous preterm birth (sPTB) is a primary cause of adverse neonatal outcomes. The objective of this study is to analyze the factors influencing the occurrence of sPTB in pregnant women and to construct and validate a predictive model for sPTB risk based on big data from clinical and laboratory assessments during pregnancy., Methods: A retrospective analysis was conducted on the clinical data of 3,082 pregnant women, categorizing those who delivered before 37 weeks of gestation as the sPTB group and those who delivered at or after 37 weeks as the full-term group. The performance of five machine learning models was compared using metrics such as the AUC, accuracy, sensitivity, specificity, and precision to identify the optimal predictive model. The top 10 predictive variables were selected based on their significance in disease prediction. The data were then divided into a training set (70%) and a validation set (30%) for validation. External data were also utilized to validate the model's predictive performance., Results: A total of 24 indicators with significant differences were identified. In terms of predicting the risk of preterm birth, the XGBoost algorithm demonstrated the most outstanding performance, with an AUC ROC of 0.89 (95% CI: 0.88-0.90). The top 10 critical indicators included ALP, AFP, ALB, HCT, TC, DBP, ALT, PLT, height, and SBP, which are essential for constructing an accurate predictive model. The model exhibited stable performance on both the training and validation sets, with AUC values of 0.93 and 0.87, respectively. Furthermore, the external testing set also showed superior performance, with an AUC of 0.79., Conclusions: At the time of delivery, ALP, AFP, ALB, HCT, TC, DBP, ALT, PLT, height, and SBP are influential factors for sPTB in pregnant women. The XGBoost algorithm, constructed based on these factors, demonstrated the most outstanding performance., Competing Interests: Declarations Ethics approval and consent to participate This study received approval from the Ethics Committee of the Hangzhou Women's Hospital (Medical Ethics Review A [004], 2024). As this was a retrospective study, no additional adverse effects on patients were observed, and patient information and data were adequately protected. The Ethics Committee of the Hangzhou Women’s Hospital approved the waiver of informed consent for the patients. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

The National Institute of Mental Health created the National Database for Autism Research (NDAR) to accelerate autism knowledge through data sharing and collaboration. However, our experience using NDAR reveals systematic challenges across several aspects of data submission, selection, management, and analysis that limit utility of this resource. We describe our NDAR experience in an ongoing project examining autism intervention outcomes among marginalized racial, ethnic, and gender groups. For this study, we planned to gather data from NDAR to conduct an individual participant data meta-analysis. Eighteen studies met inclusion criteria and reported data on participants at more than one point in time on the Vineland Adaptive Behavior Scales (Vineland) and the Autism Diagnostic Observation Schedule (ADOS). The difficulties with submitting, selecting, downloading, and managing data from NDAR posed limitations on data availability and analysis. Of the 3,850 unique participants in the selected studies, data at multiple time points were available for 312 participants on the Vineland and 278 on the ADOS. No participants had data on all assessment domains. To accelerate autism research via data sharing and collaboration with NDAR necessitates improving the processes for submitting, selecting, and managing data., Competing Interests: Declarations Competing Interests Dr. Scahill has served as a consultant to Roche, Janssen, Impel. He has received royalties from American Psychological Association, Guilford and Oxford University Press and licensing fees from Cogstate, Abbvie, Yamo. The remaining authors have no relevant financial or non-financial interests to disclose. Ethical Approval The Boston University Medical Campus Institutional Review Board approved this project as exempt. Consent to Participate Data for this commentary were drawn from the National Database for Autism Research (NDAR) data repository, available on National Institute of Mental Health National Data Archive. All participants were consented at the time of data collection in the original study before deposition in NDAR., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Numerous Deep Learning (DL) scenarios have been developed for evolving new healthcare systems that leverage large datasets, distributed computing, and the Internet of Things (IoT). However, the data used in these scenarios tend to be noisy, necessitating the incorporation of robust pre-processing techniques, including data cleaning, preparation, normalization, and addressing imbalances. These steps are crucial for generating a robust dataset for training. Designing frameworks capable of handling such data without compromising efficiency is essential to ensuring robustness. This research aims to propose a novel healthcare framework that selects the best features and enhances performance. This robust deep learning framework, called (R-DLH2O), is designed for forecasting cardiovascular crises. Unlike existing methods, R-DLH2O integrates five distinct phases: robust pre-processing, feature selection, feed-forward neural network, prediction, and performance evaluation. This multi-phase approach ensures superior accuracy and efficiency in crisis prediction, offering a significant advancement in healthcare analytics. H2O is utilized in the R-DLH2O framework for processing big data. The main improvement of this paper lies in the unique form of the Whale Optimization Algorithm (WOA), specifically the Modified WOA (MWOA). The Gaussian distribution approach for random walks was employed with the diffusion strategy to choose the optimal MWOA solution during the growth phase. To validate the R-DLH2O framework, six performance tests were conducted. Surprisingly, the MWOA-2 outperformed other heuristic algorithms in speed, despite exhibiting lower accuracy and scalability. The suggested MWOA was further analyzed using benchmark functions from CEC2005, demonstrating its advantages in accuracy and robustness over WOA. These findings highlight that the framework's processing time is 436 s, mean per-class error is 0.150125, accuracy 95.93%, precision 92.57%, and recall 93.6% across all datasets. These findings highlight the framework's potential to produce significant and robust results, outperforming previous frameworks concerning time and accuracy., Competing Interests: Declarations Competing interests The authors declare no competing interests. Ethics approval The clinical data used for this research was obtained from publicly available deidentified database, Medical Information Mart for Intensive Care (MIMIC-II), and does not require a separate ethics approval or consent obtaining process., (© 2024. The Author(s).)

Small interfering RNA (siRNA) has revolutionised biomedical research and drug development through precise post-transcriptional gene silencing technology. Despite its immense potential, siRNA therapy still faces technical challenges, such as delivery efficiency, targeting specificity, and molecular stability. To address these challenges and facilitate siRNA drug development, we have developed siRNAEfficacyDB, a comprehensive database that integrates experimentally validated siRNA efficacy data. This database contains 3544 siRNA records, covering 42 target genes and 5 cell lines. It provides detailed information on siRNA sequences, target genes, inhibition efficiencies, experimental techniques, cell lines, siRNA concentrations, and incubation times. siRNAEfficacyDB offers a user-friendly web interface that makes it easy to query, browse and analyse data, enabling efficient access to siRNA-related information. In summary, siRNAEfficacyDB provides a useful data foundation for siRNA drug design and optimisation, serving as a valuable resource for advancing computer-aided siRNA design research and nucleic acid drug development. siRNAEfficacyDB is freely available at https://cellknowledge.com.cn/siRNAEfficacy for non-commercial use., (© 2024 The Author(s). IET Systems Biology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.)

The treatment of craniomaxillofacial (CMF) defects and deformities is highly challenging due to the complex anatomical structures, significant individual variations and high demands for personalized reconstruction. In recent decades, the integration of cutting-edge technologies from fields such as computer science, mechanical engineering, and material science into medicine has greatly facilitated the transformation of CMF surgery from traditional, experience-based approaches to digitalized and intelligent methods. This review thoroughly analyzes the development and application areas of digital technology in CMF surgery including virtual surgical planning and facial prediction, three-dimensional printing, computer-assisted navigation and robotic surgery, virtual and mixed reality, as well as big data and artificial intelligence technologies. The aim is to offer comprehensive and in-depth reference information for the clinical practice in digital CMF surgery.

Drug discovery is a complex and multifaceted process aimed at identifying new therapeutic compounds with the potential to treat various diseases. Traditional methods of drug discovery are often time-consuming, expensive, and characterized by low success rates. Because of this, there is an urgent need to improve the drug development process using new technologies. The integration of the current state-of-art of artificial intelligence (AI) and machine learning (ML) approaches with conventional methods will enhance the efficiency and effectiveness of pharmaceutical research. This review highlights the transformative impact of AI and ML in drug discovery, discussing current applications, challenges, and future directions in harnessing these technologies to accelerate the development of innovative therapeutics. We have discussed the latest developments in AI and ML technologies to streamline several stages of drug discovery, from target identification and validation to lead optimization and preclinical studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The exposome encompasses the full range of environmental exposures throughout a person's lifetime and plays an important role in cardiovascular health. Interactions with the social, natural, and built components of the exposome significantly impact cardiovascular disease prevalence and mortality. Robust data analytics, including machine learning and geospatial analysis, have advanced our understanding of how these factors converge to influence cardiovascular disease risk. The integration of multiomics platforms and advanced computational approaches enhances our ability to characterize the exposome, leading to targeted public health interventions and innovative risk reduction strategies aimed at improving cardiovascular health globally. These multiomics platforms that integrate factors such as genomics, epigenomics, clinical data, social factors, environmental factors, and wearable technology will characterize the exposome in greater detail concerning cardiovascular health. In this review, we aimed to elucidate the components of the exposome and discuss recent literature regarding their relationship to cardiovascular health., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2024 The Author(s).)

TikTok employs sophisticated algorithms to deliver users increasingly personalised content over time. We investigated the potential for these algorithms to exacerbate eating disorder symptoms by analysing 1.03 million TikTok videos delivered to 42 individuals with eating disorders (76 % anorexia nervosa) and 49 healthy controls over one month. Within this video corpus, we identified four video categories relevant to eating disorder psychopathology: appearance-oriented videos, dieting videos, exercise videos, and toxic eating disorder (akin to "pro-anorexia") videos. Multi-level models predicted the likelihood of users' algorithms delivering these videos and the likelihood of users "liking" (i.e., volitionally engaging with) these videos. Algorithms belonging to users with eating disorders delivered more appearance-oriented (+146 %), dieting (+335 %), exercise (+142 %), and toxic eating disorder videos (+4343 %). Stronger biases in users' algorithms toward these videos were associated with more severe eating disorder symptoms. Whilst users with eating disorders were slightly more likely to "like" these problematic video categories (e.g., dieting videos: +23 % versus controls), their algorithms were far more likely to deliver these videos in the first place (dieting videos: +335 % versus controls). Our results provide preliminary evidence that the TikTok algorithm might exacerbate eating disorder symptoms via content personalisation processes that are desensitised to volitional user actions (i.e., "liking" videos)., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Scott Griffiths reports financial support was provided by National Health and Medical Research Council. Scott Griffiths reports financial support was provided by Medical Research Future Fund. Scott Griffiths reports a relationship with TikTok that includes: consulting or advisory. Scott Griffiths reports relationships with various education- and mental health-related organisations that includes: speaking fees, consulting or advisory., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Many studies have confirmed that PM 2.5 exposure can cause a variety of diseases. Because people spend most of their time indoors, exposure to PM 2.5 in indoor environments is critical to population health. Large-population, long-term, continuous, and accurate indoor PM 2.5 data are important but scarce because of the difficulties in monitoring the indoor air quality on a large scale. Model simulation provides a new research direction. In this study, an advanced machine learning model was constructed using environmental health big data to predict the daily indoor PM 2.5 concentration data in 22 typical air pollution cities in China from 2013 to 2017. The test R 2 value of this model reached as high as 0.89, and the RMSE of the model was 9.13. The predicted annual indoor PM 2.5 concentrations of the cities ranged from 54.6 μg/m 3 to 82.7 μg/m 3 , and showed a decreasing trend year by year. The pollution level exceeds the recommended AQG level of PM 2.5 and has potential impact on human health. The results could take a breakthrough in obtaining accurate big data of indoor PM 2.5 and contribute to research on the indoor air quality and human health in China. SYNOPSIS: This study established a machine learning model and predicted indoor PM 2.5 big data, which could support the research of indoor PM 2.5 and health., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Background: Computational Social Science (CSS) utilizes large digital datasets and computational methods to study human behavior, raising ethical concerns about data privacy, informed consent, and potential misuse. Methods: This study employs a constructivist grounded theory approach, analyzing 15 in-depth interviews with CSS practitioners in Germany, Austria, and Switzerland. These countries share a European legal context regarding data privacy and hereby provide a comparable regulatory environment for examining ethical considerations. Results: Findings highlight key challenges in CSS research, including power imbalances with data providers, uncertainties around surveillance and data privacy (especially with longitudinal data), and limitations of current ethics frameworks. Researchers face tensions between established ethical principles and practical realities, often feeling disempowered and lacking support from ethics boards due to their limited CSS expertise. Regulatory ambiguity further discourages research due to fear of sanctions. Conclusions: To foster responsible CSS practices, this paper recommends establishing specialized ethics boards with CSS expertise. It also advocates for acknowledging CSS's unique nature in research policy by developing tailored data guidelines and providing legal certainty through clear guidelines. Grounding recommendations in practitioners' experiences, this study offers actionable steps to help enable ethical CSS research.

Background: Having a primary care provider is associated with better care experiences and lower care costs. In 2021, INSPIRE-PHC released Primary Care Data Reports - publicly available summaries of administrative billing data about how populations in each of Ontario's 60 health teams use primary care services. Given the characterization of Canadian primary care systems as 'in crisis', publicly available data about primary care at the regional level presented a significant opportunity for knowledge mobilization. An understandable resource could ground the public conversation about primary care access in data. Recognizing the role that lived experience plays in ensuring the public understands research findings, a partnership between patient advisors, Ontario Health Team representatives, researchers, and trainees was established to co-produce public-facing infographics based on primary care data., Methods: Evidence-based guidelines for public health infographic creation and elements of transformative action research guided a six-meeting process to engage up to 14 patient advisors, three Ontario Health Team staff and two primary care trainees. Patient advisors were affiliated with a provincial patient-oriented primary health care research group or a Hamilton-based Ontario Health Team. Ninety-minute meetings were conducted virtually, and notes were shared with attendees to ensure they accurately reflected the conversation. Two consultations with Ontario Health Team-affiliated primary care providers provided direction and ensured project outputs aligned with local priorities., Results: Project partners shared feedback on draft infographics, audience identification, priority elements from Primary Care Data Reports to include in the infographics, and aesthetic features (e.g., headings, colour scheme, charts). Project partners felt the most important metrics to convey to the public were those that simultaneously reinforced the benefits of primary care on individual health outcomes and health system costs., Conclusions: Patient engagement in research is becoming widespread, but co-developing knowledge products with patient and health system partners is less common. Our approach to engaging patients prevented both oversimplification and unnecessary complexity in a public-facing visual about attachment to primary care., (© 2024. The Author(s).)

Breast cancer is the most prevalent and deadliest cancer among women globally, representing a major threat to public health. In response, the World Health Organization has established the Global Breast Cancer Initiative framework to reduce breast cancer mortality through global collaboration. The integration of big data analytics (BDA) and precision medicine has transformed our understanding of breast cancer's biological traits and treatment responses. By harnessing large-scale datasets - encompassing genetic, clinical, and environmental data - BDA has enhanced strategies for breast cancer prevention, diagnosis, and treatment, driving the advancement of precision oncology and personalised care. Despite the increasing importance of big data in breast cancer research, comprehensive studies remain sparse, underscoring the need for more systematic investigation. This review evaluates the contributions of big data to breast cancer precision medicine while addressing the associated opportunities and challenges. Through the application of big data, we aim to deepen insights into breast cancer pathogenesis, optimise therapeutic approaches, improve patient outcomes, and ultimately contribute to better survival rates and quality of life. This review seeks to provide a foundation for future research in breast cancer prevention, treatment, and management., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Marine microbial corrosion poses a significant threat to the safe service of marine engineering equipment. Previous studies have often failed to thoroughly analyze the continuous and prolonged microbial corrosion process, resulting in an incomplete understanding of microbial corrosion mechanisms involved at various stages and the development of ineffective control strategies. This study employed a corrosion big data online real-time monitoring technique to investigate the time-dependent corrosion behavior of EH36 steel caused by Pseudomonas aeruginosa in aerobic environments over a 30-d incubation period. It was found that P. aeruginosa accelerated the corrosion of EH36 steel in the early stages by enhancing the cathodic oxygen reduction process. As oxygen levels declined, P. aeruginosa transitioned from aerobic to anaerobic respiration, promoting corrosion through biocatalytic nitrate reduction. In the later stages, the reduction in sessile cell counts, extreme low oxygen concentration, and dense surface film increased the charge transfer and film resistances, ultimately leading to corrosion inhibition. The weight loss and electrochemical data confirmed the effectiveness of the big data monitoring technique in investigating microbial corrosion, which provides new approaches for diagnosing and preventing microbial corrosion., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Purpose: To investigate the prevalence of anogenital warts (AGW) caused by human papillomavirus in men and the characteristics and changes of the disease in Korea., Materials and Methods: From 2007 to 2018, male AGW corresponding to the International Classification of Disease-10 A63.0 were targeted. Through a big data analysis of the Health Insurance Review & Assessment Service, data of patients diagnosed with AGW as either the main or secondary diagnosis were extracted. The overall prevalence and prevalence by age were calculated through the population by year and the population by 5-year-old unit of the National Statistical Office. Additionally, the amount accrued through the disease code was confirmed and compared as AGW-related medical expenses., Results: The annual number of patients increased by 3.5 times from 32,709 in 2007 to 114,734 in 2018. Additionally, the annual prevalence increased by 3.34 times from 132.83 per 100,000 to 443.57. The age-specific prevalence was the highest among the 25- to 29-year age group. The highest prevalence was observed in the 25- to 29-year age group in 2018 (1,492.15 per 100,000), and the lowest was noted in the 10- to 14-year age group in 2014 (7.28 per 100,000). AGW-related medical expenses increased by approximately 9.76 times from US$ 2,789,215.1 in 2007 to US$ 27,227,254.9 in 2018., Conclusions: In Korea, cases of male AGW are increasing yearly, with the 25- to 29-year age group having the highest prevalence. Medical costs related to AGW are also increasing tenfold. For male AGW, further studies and national interventions are needed., Competing Interests: The authors have nothing to disclose., (© The Korean Urological Association.)

Advanced practice nurse leaders are in key positions within health systems to provide time and resources for implementation and evaluation of digital health services. As virtual monitoring programs become more embedded within nursing, nurse leaders and educators need to ensure that nurses are prepared to work within interprofessional teams to administer and evaluate them. This article discusses challenges and implementation strategy considerations for data curation and analysis using large datasets from the Medicaid population for research., Competing Interests: J.A.H. is chief executive officer and a shareholder in Keylog Solutions LLC; has received funding from Pfizer; is a shareholder in other healthcare companies including Whispersom Corporation, EmOpti LLC, HealthLab LLC, and Baxter Healthcare; serves in an advisory role to Whispersom Corporation, EmOpti LLC, and HealthLab LLC; and has various patents that have been granted or are pending. The remaining authors declare no conflicts of interest. The OSF Medicaid Innovation Collaborative (MIC) digital health programs are supported in part by the Illinois Department of Healthcare and Family Services as part of the Department's Healthcare Transformation Collaborative Program, USA (grant number not applicable). The MIC research study is supported by the OSF HealthCare Foundation, Peoria, Illinois. Funding supports only qualitative interviews (not described here) (grant number not applicable)., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

The rise of large, publicly shared functional magnetic resonance imaging (fMRI) data sets in human neuroscience has focused on acquiring either a few hours of data on many individuals ('wide' fMRI) or many hours of data on a few individuals ('deep' fMRI). In this opinion article, we highlight an emerging approach within deep fMRI, which we refer to as 'intensive' fMRI: one that strives for extensive sampling of cognitive phenomena to support computational modeling and detailed investigation of brain function at the single voxel level. We discuss the fundamental principles, trade-offs, and practical considerations of intensive fMRI. We also emphasize that intensive fMRI does not simply mean collecting more data: it requires careful design of experiments to enable a rich hypothesis space, optimizing data quality, and strategically curating public resources to maximize community impact., Competing Interests: Declaration of interests The authors declare no competing interests in relation to this work., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Current interventions for oral/dental diseases heavily rely on operative/surgical procedures, while the discovery of novel drug targets may enable access to noninvasive pharmacotherapy. Therefore, this study aims to leverage large-scale data and Mendelian randomization (MR) techniques, utilizing genetic variants as instruments, to identify potential therapeutic targets for oral and dental diseases supported by genetic evidence. By intersecting 4,302 druggable genes with expression quantitative trait loci from 31,684 blood samples, we identified 2,580 druggable targets as exposures. Single nucleotide polymorphisms associated with dental disease/symptom traits were collected from FinnGen R9, the Gene-Lifestyle Interactions in Dental Endpoints consortium, and the UK Biobank to serve as outcomes for both discovery and replication purposes. Through MR analysis, we identified 43 druggable targets for various dental disease/symptom traits. To evaluate the viability of these targets, we replicated the analysis using circulating protein quantitative trait loci as exposures. Additionally, we conducted sensitivity, colocalization, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes annotation, protein-protein interaction analyses, and validated dental trait-associated druggable gene expression in animal models. Among these targets, IL12RB1 (odds ratio [OR], 1.01; 95% confidence interval [CI], 1.01-1.01) and TNF (OR, 0.98; 95% CI, 0.97-0.99) exhibited therapeutic promise for oral ulcers, whereas CXCL10 (OR, 0.84; 95% CI, 0.76-0.91) was for periodontitis. Through a rigorous quality control and validation pipeline, our study yields compelling evidence for these druggable targets, which may enhance the clinical prognosis by developing novel drugs or repurposing existing ones., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose of Review: Patient privacy protection is a critical focus in medical practice. Advances over the past decade in big data have led to the digitization of medical records, making medical data increasingly accessible through frequent data sharing and online communication. Periocular features, iris, and fundus images all contain biometric characteristics of patients, making privacy protection in ophthalmology particularly important. Consequently, privacy-preserving technologies have emerged, and are reviewed in this study., Recent Findings: Recent findings indicate that general medical privacy-preserving technologies, such as federated learning and blockchain, have been gradually applied in ophthalmology. However, the exploration of privacy protection techniques of specific ophthalmic examinations, like digital mask, is still limited. Moreover, we have observed advancements in addressing ophthalmic ethical issues related to privacy protection in the era of big data, such as algorithm fairness and explainability., Summary: Future privacy protection for ophthalmic patients still faces challenges and requires improved strategies. Progress in privacy protection technology for ophthalmology will continue to promote a better healthcare environment and patient experience, as well as more effective data sharing and scientific research., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose of Review: Health data sciences can help mitigate high burden of cardiovascular disease (CVD) management in South Asia by increasing availability and affordability of healthcare services. This review explores the current landscape, challenges, and strategies for leveraging digital health technologies to improve CVD outcomes in the region., Recent Findings: Several South Asian countries are implementing national digital health strategies that aim to provide unique health account numbers for patients, creating longitudinal digital health records while others aim to digitize healthcare services and improve health outcomes. Significant challenges impede progress, including lack of interoperability, inadequate training of healthcare workers, cultural barriers, and data privacy concerns. Leveraging digital health for CVD management involves using big data for early detection, employing artificial intelligence for diagnostics, and integrating multiomics data for health insights. Addressing these challenges through policy frameworks, capacity building, and international cooperation is crucial for improving CVD outcomes in region., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose of Review: The rising burden of cardiovascular disease (CVD) in Africa is of great concern. Health data sciences is a rapidly developing field which has the potential to improve health outcomes, especially in low-middle income countries with burdened healthcare systems. We aim to explore the current CVD landscape in Africa, highlighting the importance of health data sciences in the region and identifying potential opportunities for application and growth by leveraging health data sciences to improve CVD outcomes., Recent Findings: While there have been a number of initiatives aimed at developing health data sciences in Africa over the recent decades, the progress and growth are still in their early stages. Its maximum potential can be leveraged through adequate funding, advanced training programs, focused resource allocation, encouraging bidirectional international partnerships, instituting best ethical practices, and prioritizing data science health research in the region. The findings of this review explore the current landscape of CVD and highlight the potential benefits and utility of health data sciences to address CVD challenges in Africa. By understanding and overcoming the barriers associated with health data sciences training, research, and application in the region, focused initiatives can be developed to promote research and development. These efforts will allow policymakers to form informed, evidence-based frameworks for the prevention and management of CVDs, and ultimately result in improved CVD outcomes in the region., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Ascertaining the presence of weakly positive anti-HLA donor-specific antibodies (DSA) in organ transplantation with multiplex single antigen beads assays may be challenging despite their high sensitivity due to technical variability issues. Through extensive datasets of Next-Generation Sequencing HLA typings and single antigen analyses, we reassessed the mean fluorescence intensity (MFI) positivity threshold of the assay to enhance accuracy. By showing that some beads were more prone to false positivity than others, we propose a nuanced approach that accounts for nonspecific intrinsic reactivities at the HLA antigen level, that is, on a bead-by-bead basis, as it enhances assay precision and reliability. This is substantiated by a comprehensive statistical analysis of MFI values and the implementation of the determination of a "Quantile Adjusted Threshold 500" (QAT500) value for each bead. Applied to DSA detection during patients' follow-up, this approach discriminated better and earlier low-strength DSA that would later raise their MFI above the clinically relevant threshold of 3000. Moving from a subjective interpretation to a more objective and precise methodology allows for standardizing HLA antibody and DSA detection. The study emphasizes the need for further research with real clinical data to validate and refine this approach., (© 2024 The Author(s). European Journal of Immunology published by Wiley‐VCH GmbH.)

Introduction: Cardiovascular complications of diabetes are a top cause of death in diabetics and often involve immune system problems. Despite numerous studies, there's a shortage of extensive data to advance this field. This study aims to systematically analyze the role of immune dysregulation in these complications using bibliometric methods, to outline the research path and predict future directions., Methods: Published from January 1, 2014 to December 31, 2023, 2826 records from the Web of Science Core Collection were analyzed. Collaboration networks, keyword co-occurrences, references, and research hotspots were visualized and analyzed using Microsoft Office Excel 2019, VOSviewer, CiteSpace, and R software., Results: The number of research papers and citations on this topic has been increasing from 2014 to 2023, with significant contributions from the United States and China. Studies have focused on the effects of oxidative stress, inflammation, metabolism, gut microbiota, and COVID-19 on diabetic heart problems, highlighting the role of immune dysregulation in these diseases., Conclusion: This research provides an overview of immune dysregulation in the cardiovascular complications of diabetes, explores potential treatments including immunomodulation, insulin resistance, and the benefits of vitamin D on cardiovascular disease, and helps advance the field., Competing Interests: Declaration of Competing Interest The author had no conflict of interest to disclose., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objectives: To assess the association between overactive bladder syndrome (OAB) and the metabolic syndrome (MetS)., Patients and Methods: A population-based study was conducted to compare OAB patients with age-, sex- and ethnicity-matched control subjects regarding the prevalence of the parameters of the MetS, with respect to obesity, hyperlipidemia, hypertension and diabetes mellitus. The characteristics of the OAB population were assessed. Adjusted odds ratios (OR) were calculated by logistic regression., Results: 110 024 OAB patients and 220 455 controls. were identified. OAB was associated with a higher prevalence of MetS (35.4% vs. 27.5%, p < 0.001). The fully adjusted OR for MetS in patients with OAB compared to controls was 1.44; 95% confidence interval (CI) 1.42-1.46; p < 0.001. Among metabolic parameters, obesity was found to be the strongest factor associated with OAB (OR 1.55, 95% CI 1.53-1.58, p < 0.001), and higher high-density lipoprotein cholesterole levels (>50) had a protective effect on the risk of OAB (OR 0.75, 95% CI 0.73-0.76, p < 0.001)., Conclusions: Data from this cohort suggest that OAB is positively associated with MetS. Clinicians approaching patients with OAB should be aware of this association. A multimodal treatment focusing on the MetS may be considered in these patients., (© 2024 Wiley Periodicals LLC.)

Purpose: The current study queries the American Academy of Ophthalmology (AAO) Intelligent Research in Sight (IRIS) registry for data on the epidemiology, work-up, and management patterns of autoimmune orbital inflammation., Methods: Analysis and description of patient data from the IRIS registry between 2013 and 2019 reviewing patients with autoimmune or idiopathic orbital inflammation with filters based on International Classification of Disease (ICD) and Current Procedural Terminology (CPT) codes. Patients with thyroid eye disease, orbital cellulitis, and orbital abscess were excluded., Main Outcome Measures: Demographic descriptions included gender, age, geographic region, and treatment. Sub-analysis was performed by assessing rates of imaging, biopsy, lab work-up, and diagnostic categories., Results: In a final cohort of 20,584 patients, the mean age of onset of orbital inflammation was 51.7 years; 67% female; and 63% Caucasian, 21% unknown, 12% Black, 2.6% Asian, and 1.5% other. Only 49 had imaging, 78 had laboratory work-up, and 1,411 had biopsy codes. Treatment results showed 166 patients receiving antibiotics, 224 patients receiving steroids, and 35 patients receiving both., Conclusions: This study assessed the epidemiology, diagnostic patterns, and treatment patterns for orbital inflammation through the AAO IRIS registry. Practise patterns suggest a relatively low overall rate of imaging and laboratory studies compared to biopsies, although this certainly under-represents the actual number of imaging and laboratory studies and exemplifies the inherent imprecision of using a large database. However, the methodology of this study provides a framework of approaching the IRIS registry for oculoplastic research.

Background: Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes., Purpose: These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems., Methods: Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues., Results: Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research., Conclusion: These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: Integrating machine learning models into electronic medical record systems can greatly enhance decision-making, patient outcomes, and value-based care in healthcare systems. Challenges related to data accessibility, privacy, and sharing can impede the development and deployment of effective predictive models in spine surgery. Federated learning (FL) offers a decentralized approach to machine learning that allows local model training while preserving data privacy, making it well-suited for healthcare settings. Our objective was to describe federated learning solutions for enhanced predictive modeling in spine surgery., Methods: The authors reviewed the literature., Results: FL has promising applications in spine surgery, including telesurgery, AI-based prediction models, and medical image segmentation. Implementing FL requires careful consideration of infrastructure, data quality, and standardization, but it holds the potential to revolutionize orthopedic surgery while ensuring patient privacy and data control., Conclusions: Federated learning shows great promise in revolutionizing predictive modeling in spine surgery by addressing the challenges of data privacy, accessibility, and sharing. The applications of FL in telesurgery, AI-based predictive models, and medical image segmentation have demonstrated their potential to enhance patient outcomes and value-based care., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis., (© 2024. The Author(s).)

Background: Healthcare 4.0. refers to the integration of advanced technologies, such as artificial intelligence (AI) and big data analysis, into the healthcare sector. Recognizing the impact of Healthcare 4.0 technologies in laboratory medicine (LM), we seek to assess the overall awareness and implementation of Healthcare 4.0 among members of the Korean Society for Laboratory Medicine (KSLM)., Methods: A web-based survey was conducted using an anonymous questionnaire. The survey comprised 36 questions covering demographic information (seven questions), big data (10 questions), and AI (19 questions)., Results: In total, 182 (17.9%) of 1,017 KSLM members participated in the survey. Thirty-two percent of respondents considered AI to be the most important technology in LM in the era of Healthcare 4.0, closely followed by 31% who favored big data. Approximately 80% of respondents were familiar with big data but had not conducted research using it, and 71% were willing to participate in future big data research conducted by the KSLM. Respondents viewed AI as the most valuable tool in molecular genetics within various divisions. More than half of the respondents were open to the notion of using AI as assistance rather than a complete replacement for their roles., Conclusions: This survey highlighted KSLM members' awareness of the potential applications and implications of big data and AI. We emphasize the complexity of AI integration in healthcare, citing technical and ethical challenges leading to diverse opinions on its impact on employment and training. This highlights the need for a holistic approach to adopting new technologies.

Background: The changes in body composition during puberty not only contribute to the differences in body composition between adult males and females but also have associations with growth problems and metabolic disorders, including obesity. Therefore, understanding the changes in body composition during the pubertal period and analyzing reference values based on race and gender are essential research resources. The objective of this study was to generate reference centiles for body composition on a monthly basis using an extensive dataset of body composition information from Korean children and adolescents., Methods: A total of 88,069 measurements from 22,515 children (11,062 boys and 11,453 girls) aged 7-16 years using a bioelectrical impedance analysis were included in the study after performing a Z-score-based data management procedure. Height, weight, body fat mass (BFM), and fat-free mass (FFM) were measured and used to derive body fat percentage (BF%), body mass index (BMI), fat mass index (FMI), and fat-free mass index (FFMI). Sex- and age-specific centiles were estimated using generalized additive models for location, scale, and shape with the Box-Cox Cole and Green distribution (i.e., lambda-mu-sigma method)., Results: The sex- and age-related disparities in body composition were most pronounced when weight was partitioned into BFM and FFM. In boys, the FFM increased markedly during pubertal growth spurts, whereas BFM remained relatively stable. In girls, the BFM increased steadily, whereas the rate of FFM increased slowly. The BMI increased steadily with age in both sexes. However, when BMI was parsed into FMI and FFMI, it became clear that the FFMI increased substantially during pubertal growth in boys, whereas the FMI peaked around age 11 and then declined. Conversely, the FMI increased steadily in girls, albeit with a slowing rate in the increase of the FFMI beginning around age 12., Conclusions: This study produced age- and sex-specific reference percentiles for body composition indices in Korean children and adolescents using extensive biometric data., (© 2024. The Author(s).)

Artificial Intelligence is playing a crucial role in healthcare by enhancing decision-making and data analysis, particularly during the COVID-19 pandemic. This virus affects individuals across all age groups, but its impact is more severe on the elderly and those with underlying health issues like chronic diseases. This study aimed to develop a machine learning model to improve the prediction of COVID-19 in patients with acute respiratory symptoms. Data from 915 patients in two hospitals in Saudi Arabia were used, categorized into four groups based on chronic lung conditions and COVID-19 status. Four supervised machine learning algorithms-Random Forest, Bagging classifier, Decision Tree, and Logistic Regression-were employed to predict COVID-19. Feature selection identified 12 key variables for prediction, including CXR abnormalities, smoking status, and WBC count. The Random Forest model showed the highest accuracy at 99.07%, followed by Decision Tree, Bagging classifier, and Logistic Regression. The study concluded that machine learning algorithms, particularly Random Forest, can effectively predict and classify COVID-19 cases, supporting the development of computer-assisted diagnostic tools in healthcare., (© 2024 The Author(s). IET Systems Biology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.)

Total Quality Management (TQM) is instrumental in augmenting the quality and efficacy of healthcare service delivery, but a comprehensive evaluation of present and evolving TQM research trends within healthcare research articles is notably absent. This study provides an insightful view into the prevailing international scenarios and upcoming research frontiers in healthcare TQM research field, utilizing bibliometric mapping through VOSviewer. Drawing data from 360 publications in the Web of Science core citation database, it delineates a steady growth in the field over the last 30 years. Research outputs span 51 countries and regions, with notable contributions from the United States, United Kingdom, Netherlands, and Italy. The top five research institutions and numerous authors predominantly hail from the United States. Key keywords in near years encompass healthcare safety, healthcare quality assurance, quality indicators, and the application of Six Sigma management principles. This exploration serves as a pivotal reference for understanding the global research landscape and future trends in healthcare TQM, particularly in guaranteeing quality and safety. Future scientific endeavors will build upon these focus areas, exploring and connecting research gaps in more specialized fields., (© 2024. The Author(s).)

Background: The rapid advancement of digital technologies, particularly in big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL), is reshaping the global health care system, including in Bangladesh. The increased adoption of these technologies in health care delivery within Bangladesh has sparked their integration into health care and public health research, resulting in a noticeable surge in related studies. However, a critical gap exists, as there is a lack of comprehensive evidence regarding the research landscape; regulatory challenges; use cases; and the application and adoption of BDA, AI, ML, and DL in the health care system of Bangladesh. This gap impedes the attainment of optimal results. As Bangladesh is a leading implementer of digital technologies, bridging this gap is urgent for the effective use of these advancing technologies., Objective: This scoping review aims to collate (1) the existing research in Bangladesh's health care system, using the aforementioned technologies and synthesizing their findings, and (2) the limitations faced by researchers in integrating the aforementioned technologies into health care research., Methods: MEDLINE (via PubMed), IEEE Xplore, Scopus, and Embase databases were searched to identify published research articles between January 1, 2000, and September 10, 2023, meeting the following inclusion criteria: (1) any study using any of the BDA, AI, ML, and DL technologies and health care and public health datasets for predicting health issues and forecasting any kind of outbreak; (2) studies primarily focusing on health care and public health issues in Bangladesh; and (3) original research articles published in peer-reviewed journals and conference proceedings written in English., Results: With the initial search, we identified 1653 studies. Following the inclusion and exclusion criteria and full-text review, 4.66% (77/1653) of the articles were finally included in this review. There was a substantial increase in studies over the last 5 years (2017-2023). Among the 77 studies, the majority (n=65, 84%) used ML models. A smaller proportion of studies incorporated AI (4/77, 5%), DL (7/77, 9%), and BDA (1/77, 1%) technologies. Among the reviewed articles, 52% (40/77) relied on primary data, while the remaining 48% (37/77) used secondary data. The primary research areas of focus were infectious diseases (15/77, 19%), noncommunicable diseases (23/77, 30%), child health (11/77, 14%), and mental health (9/77, 12%)., Conclusions: This scoping review highlights remarkable progress in leveraging BDA, AI, ML, and DL within Bangladesh's health care system. The observed surge in studies over the last 5 years underscores the increasing significance of AI and related technologies in health care research. Notably, most (65/77, 84%) studies focused on ML models, unveiling opportunities for advancements in predictive modeling. This review encapsulates the current state of technological integration and propels us into a promising era for the future of digital Bangladesh., (©Md Ashraful Alam, Md Refat Uz Zaman Sajib, Fariya Rahman, Saraban Ether, Molly Hanson, Abu Sayeed, Ema Akter, Nowrin Nusrat, Tanjeena Tahrin Islam, Sahar Raza, K M Tanvir, Mohammod Jobayer Chisti, Qazi Sadeq-ur Rahman, Akm Hossain, MA Layek, Asaduz Zaman, Juwel Rana, Syed Moshfiqur Rahman, Shams El Arifeen, Ahmed Ehsanur Rahman, Anisuddin Ahmed. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.10.2024.)

Background: Clinical coding allows for structured and standardised recording of patients' electronic healthcare records. How clinical and non-clinical staff in general practice approach clinical coding is poorly understood., Aim: To explore primary care staff's experiences and views on clinical coding., Design and Setting: Qualitative, semi-structured interview study among primary care staff across Wales., Method: All general practices within Wales were invited to participate via NHS health boards. Semi-structured questions guided interviews, conducted between February 2023 and June 2023. Audio-recorded data were transcribed and analysed using reflexive thematic analysis., Results: A total of 19 participants were interviewed and six themes were identified: coding challenges, motivation to code, making coding easier, daily task of coding, what and when to code, and coding through COVID., Conclusion: This study demonstrates the complexity of clinical coding in primary care. Clinical and non-clinical staff spoke of systems that lacked intuitiveness, and the challenges of multimorbidity and time pressures when coding in clinical situations. These challenges are likely to be exacerbated in socioeconomically deprived areas, leading to underreporting of disease in these areas. Challenges of clinical coding may lead to implications for data quality, particularly the validity of research findings generated from studies reliant on clinical coding from primary care. There are also consequences for patient care. Participants cared about coding quality and wanted a better way of using coding. There is a need to explore technological and non-technological solutions, such as artificial intelligence, training, and education to unburden people using clinical coding in primary care., (© The Authors.)

Purpose: The burst of modern information has significantly promoted the development of global medicine into a new era of big data healthcare. Ophthalmology is one of the most prominent medical specialties driven by big data analytics. This study aimed to describe the development status and research hotspots of big data in ophthalmology., Methods: English articles and reviews related to big data in ophthalmology published from January 1, 1999, to April 30, 2024, were retrieved from the Web of Science Core Collection. The relevant information was analyzed and visualized using VOSviewer and CiteSpace software., Results: A total of 406 qualified documents were included in the analysis. The annual number of publications on big data in ophthalmology reached a rapidly increasing stage since 2019. The United States ( n  = 147) led in the number of publications, followed by India ( n  = 77) and China ( n  = 69). The L.V. Prasad Eye Institute in India was the most productive institution ( n  = 50), and Anthony Vipin Das was the most influential author with the most relevant literature ( n  = 45). The electronic medical records were the primary source of ophthalmic big data, and artificial intelligence served as the principal analytics tool. Diabetic retinopathy, glaucoma, and myopia are currently the main topics of interest in this field., Conclusions: The application of big data in ophthalmology has experienced rapid growth in recent years. Big data is expected to play an increasingly significant role in shaping the future of research and clinical practice in ophthalmology.

Background: Global health systems are confronting challenges that intersect climate change with evolving communicable and non-communicable public health risks. Addressing these challenges requires systems integration via citizen big data that exist outside health systems. However, systems integration across jurisdictions is a complex challenge that requires stakeholder input. This study's purpose was to conduct rapid systems mapping with international health system stakeholders to inform the development and implementation of a global digital citizen science observatory (DiScO), which aims to catalyze digital transformation of health systems across jurisdictions., Methods: A rapid qualitative systems mapping study was conducted during the International Society for Behavioral Nutrition and Physical Activity Annual Global Summit in Uppsala, Sweden, in June 2023. The choice of the venue and approach was informed by three key criteria: (1) Established evidence linking physical activity and nutrition with non-communicable diseases; (2) Concrete existing methods of obtaining citizen big data by physical activity and nutrition researchers; (3) Precedence of physical activity and nutrition researchers conducting citizen science as well behavioral/clinical big data collection. The design of this study was an innovative pre-post systems map development, which consisted of (1) real-time rapid systems mapping (pre/initial map) by engaging with international stakeholders and (2) adjustment of the real-time systems map (post/final map) after analyzing stakeholder discussion data., Results: Rapid systems mapping resulted in a complex network that included key themes to successfully develop and implement DiScO: priorities, opportunities, risks, challenges, partnerships, and resources. Additionally, a new theme emerged organically through stakeholder group discussions - mitigation strategies. The adapted rapid systems map (i.e., after data analyses) depicts 23 key nodes of intervention across the seven key themes., Conclusion: Rapid systems mapping at international symposia is a novel methodological approach to capture stakeholder input, particularly to understand complexity across international jurisdictions - an approach that can be replicated across disciplines and sectors to inform digital transformation of health systems. The development and implementation of DiScO, a platform for decentralization and democratization of technology, will take into consideration all the key nodes of intervention identified in the rapid systems map to promote digital health for equity across global jurisdictions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Katapally, Elsahli and Bhawra.)

This study reviews the applications of big data (BD) and blockchain technology in modern food testing and explores their impact on educational reform. The first part highlights the critical role of BD in ensuring food safety across the supply chain, discussing various data collection methods, such as national and international food safety databases, while addressing the challenges related to data storage and real-time information retrieval. Additionally, blockchain technology has been explored for its ability to enhance transparency, traceability, and security in the food-testing process by creating immutable records of testing data, ensuring data integrity, and reducing the risk of tampering or fraud. The second part focuses on the influence of BD and blockchain on educational reform, particularly within food science curricula. BD enables data-driven curriculum design, supporting personalized learning and more effective educational outcomes, while blockchain ensures transparency in course management and credentials. This study advocates integrating these technologies into curriculum reform to enhance both the efficiency and quality of education.

Aims: The integration of artificial intelligence (AI) into functional urology management must be assessed for its clinical utility, but hopefully will change, perhaps to revolutionize the way LUTD and other conditions are assessed, the aim being to offer patients more rapid and effective management which enhances patient outcomes. The aim of this proposal, discussed at the ICI-RS annual meeting, is to evaluate the available evidence on AI and the way it might change the approach to urodynamic (UDS) diagnoses, including overactive bladder syndrome (OAB), and perhaps other LUTDs such as bladder outflow obstruction., Methods: A compendium of discussion based on the current evidence related to AI and its potential applications in UDS and OAB., Results: AI-powered diagnostic tools are being developed to analyze complex datasets from urodynamic studies, imaging, and other diagnostic tests. AI systems can leverage large volumes of clinical data to recommend personalized treatment plans based on individual patient profiles to optimize surgical procedures, enhance diagnostic precision, tailor the therapy, reduce the risk of complications, and improve outcomes. In the future, AI will be able to provide tailored counseling regarding the outcomes and potential side effects of drugs and procedures to a given patient., Conclusion: AI's role in functional urology has been poorly investigated, and its implementation across several areas may improve clinical care and the pathophysiological understanding of functional urologic conditions., (© 2024 Wiley Periodicals LLC.)

Epilepsy care generates multiple sources of high-dimensional data, including clinical, imaging, electroencephalographic, genomic, and neuropsychological information, that are collected routinely to establish the diagnosis and guide management. Thanks to high-performance computing, sophisticated graphics processing units, and advanced analytics, we are now on the cusp of being able to use these data to significantly improve individualized care for people with epilepsy. Despite this, many clinicians, health care providers, and people with epilepsy are apprehensive about implementing Big Data and accompanying technologies such as artificial intelligence (AI). Practical, ethical, privacy, and climate issues represent real and enduring concerns that have yet to be completely resolved. Similarly, Big Data and AI-related biases have the potential to exacerbate local and global disparities. These are highly germane concerns to the field of epilepsy, given its high burden in developing nations and areas of socioeconomic deprivation. This educational paper from the International League Against Epilepsy's (ILAE) Big Data Commission aims to help clinicians caring for people with epilepsy become familiar with how Big Data is collected and processed, how they are applied to studies using AI, and outline the immense potential positive impact Big Data can have on diagnosis and management., (© 2024 The Author(s). Epileptic Disorders published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Otoliths are calcified structures found in the inner ears of teleost fish, pivotal in marine biology for studies on metabolism, age, growth, and the identification of fish stocks, potentially leading to sustainable management practices. An important feature of this structure is its density, as it corresponds to modifications in the crystalline form of calcium carbonate during the fish's lifetime, resulting in variations in its final shape. The internal and external 3D radiodensity of otoliths from different species was obtained utilizing micro-computed tomography, however, an appropriate methodology for describing and conducting comparative studies on these data appears to be absent in the current body of literature. Therefore, we study otolith density variations from 3D computed tomography images, employing the Ball Mapper technique of Topological Data Analysis. We focus on reducing the computational cost of this analysis by applying probabilistic sampling and assessing its effects on the density variations provided by the Ball Mapper graph. To determine the sample size, we used the topology to establish what we term "Topological Sample Validation", which provided the minimum resolution with the same density information as raw data. Sample representativeness was validated through non-parametric statistical tests on the density variable. Based on the network's structural characteristics, network properties allowed for evaluating similarity between graphs. Besides the small sample size, remarkable correlations were obtained between age and network variables. Additionally, the Ball Mapper technique proved effective as a preprocessing algorithm for tomographic images, enabling the segmentation of undesired features in the object of interest., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

To investigate the big data characteristics and trend changes of patients with allergic rhinitis (AR) who sought medical attention at our hospital before (from 2018 to 2019) and after (from 2020 to 2023) COVID-19, and provide reference basis for the treatment of AR. This study used a descriptive epidemiological method to analyze the big data and trend changes of AR patients. A total of 62,196 AR patients were collected, of whom 32,874 were male and 29,322 were female, with an age range of 1-89 years. The monthly change trend of AR patients showed a marked seasonality. The number of AR patients increased year by year. There was no significant difference in the number of patients between different genders. There was a significant difference in the number of patients between different age groups. The number of AR patients increased markedly from 2018 to 2023, and COVID-19 seems to have accelerated this process. There is a clear seasonal pattern. The number of boy patients is significantly higher than that of girl patients, and the change in female hormones may affect the incidence of AR. Therefore, it is necessary to update management measures and formulate relevant policies in response to the changing trend of AR since the COVID-19 epidemic., (© 2024. The Author(s).)

Background: The phenomenon of high-cost users (HCUs) in health care occurs when a small proportion of patients account for a large proportion of health care expenditures. By understanding this phenomenon during the COVID-19 pandemic, tailored interventions can be provided to ensure that patients receive the care they need and reduce the burden on the health system., Objectives: This study aimed to determine (1) whether the HCUs phenomenon occurred during the pandemic in Thailand by exploring the pattern of inpatient health expenditures over time from 2016 to 2021; (2) the patient characteristics of HCUs; (3) the top 5 primary diagnoses of HCUs; and (4) the potential predictors associated with being an HCU., Methods: The secondary data analysis was conducted via inpatient department (IPD) e-Claim data from the National Health Security Office for the Universal Coverage Scheme, which provides health care to ~ 80% of the Thai population. Health care expenditure over time was calculated, and the characteristics of the population were examined via descriptive analysis. Multinomial logistic regression was applied to explore the potential predictors associated with being an HCU., Results: The characteristics of HCUs remained relatively the same from 2016 to 2021. In terms of the proportion of male (55%) to female patients (45%), the age ranged from 55 to 57 years, with an estimated 8-day length of hospital stay and 7 admissions per year, and the average health care cost per patient was ≥ USD 2,860 (100,000 THB). The low-cost users (LCUs) group (the bottom 50% of the population), had more female patients (55%), a younger age ranging from 27 to 33 years, a 3-day length of stay, 1‒2 admissions per year, and a lower average health care cost per patient, which was less than USD 315 (≤ 11,000 THB)., Conclusion: The HCUs phenomenon still existed even with limited health care accessibility or lockdown measures implemented during the COVID-19 pandemic. This finding could indicate the uniqueness of the need for health services by HCUs, which differ from those of other population groups. By understanding the trends of health care utilization and expenditure, along with potential predictors associated with being an HCU, policies can be introduced to ensure the appropriate allocation of health resources to the right people in need of the right care during future pandemics., (© 2024. The Author(s).)

Background: Healthcare institutions focus on improving the quality of life for end-users, with key performance indicators like access to essential medicines reflecting the effectiveness of management. Effective healthcare management involves planning, organizing, and controlling institutions built on human resources, data systems, service delivery, access to medicines, finance, and leadership. According to the World Health Organization, these elements must be balanced for an optimal healthcare system. Big data generated from healthcare institutions, including health records and genomic data, is crucial for smart staffing, decision-making, risk management, and patient engagement. Properly organizing and analysing this data is essential, and machine learning, a sub-field of artificial intelligence, can optimize these processes, leading to better overall healthcare management., Objectives: This review examines the major applications of machine learning in healthcare management, the algorithms frequently used in data analysis, their limitations, and the evidence-based benefits of machine learning in healthcare., Methods: Following PRISMA guidelines, databases such as IEEE Xplore, ScienceDirect, ACM Digital Library, and SCOPUS were searched for eligible articles published between 2011 and 2021. Articles had to be in English, peer-reviewed, and include relevant keywords like healthcare, management, and machine learning., Results: Out of 51 relevant articles, 6 met the inclusion criteria. Identified algorithms include topic modelling, dynamic clustering, neural networks, decision trees, and ensemble classifiers, applied in areas such as electronic health records, chatbots, and multi-disease prediction., Conclusion: Machine learning supports healthcare management by aiding decision-making, processing big data, and providing insights for system improvements., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Purpose: The aim of this study was to investigate the occurrence of Dry Eye Syndrome (DES) in children under 18 years old before and during the COVID-19 pandemic using nationwide population-based cohort analysis., Methods: This study utilized the database provided by the Korea Disease Control and prevention Agency (KDCA) and the National Health Insurance Service (NHIS). We used claims-based data for patients diagnosed with coronavirus disease-2019 (COVID-19) between October 8, 2020, and December 31, 2021, and those without a diagnosis of COVID-19. DES cases were defined as having at least one diagnosis of H0411 or H1621 based on the International Classification of Diseases, 10th revision (ICD-10) codes. The primary outcome was the evaluation of the hazard ratio (HR) for DES between the COVID-19 season and the non-COVID-19 season., Results: A total of 198,486 individuals from the COVID-19 season cohort and 211,828 individuals from the non-COVID-19 season cohort were included in the study. There were no differences in characteristics between the COVID-19 season cohort and the non-COVID-19 season cohort (all SMDs < 0.1). The cumulative incidence of DES during the COVID-19 season was significantly higher than that during the non-COVID-19 season. The COVID-19 season DES incidence rate was 6419.64 per 100,000 person-years (P-Y) and non-COVID-19 season DES incidence rate was 5804.88. In addition, 13-18-year-olds, females, those in Metropolitan areas, and diabetes mellitus had a higher risk of DES., Conclusion: The prevalence of diagnosed DES in children increased during the COVID-19 pandemic compared to previous years. Children aged 13-18, females, and those living in metropolitan areas had a higher risk of DES. Further studies are needed to directly analyze the potential factors associated with the increased prevalence of DES.

To solve the difficult problems of tailings dam instability and environmental pollution, multisource information perception, prediction and early warning technology for tailings dams are investigated. Taking a tailings pond in China as an example, a three-dimensional visualization intelligent management platform based on the spatiotemporal fusion of multisource big data is established to realize intelligent real-time monitoring, prediction and early warning of tailings dams. A monitoring system for air-space-ground integration was developed via high-resolution optical image recording, unmanned aerial vehicles (UAVs), radar, video surveillance and displacement sensors. This approach can realize pollution monitoring and efficient identification of high-risk areas. In addition, a machine learning algorithm is used to mine spatiotemporal data in detail. The Alibaba cloud platform was adopted to develop a data integration framework. Multisensor spatiotemporal data from tailings dams and multisource monitoring data from the environment were integrated. The integrated management of tailings dam environmental monitoring, pollution and stability assessment is realized. A stability prediction model for tailings dams based on multisource data is proposed. The temporal and spatial information of various forms of data is analysed from multiple levels and perspectives. Rapid prediction of the disaster situation and stability of tailings ponds is realized. Moreover, a tailings pollution assessment model based on a neural network is integrated with multisource information to establish a multifactor environmental pollution neural network assessment model and a multilevel warning platform. This work can provide technical support for intelligent monitoring and early warning systems for tailings ponds., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: While predictive capabilities of machine learning (ML) algorithms for hip and knee total joint arthroplasty (TJA) have been demonstrated in previous studies, their performance in racial and ethnic minority patients has not been investigated. This study aimed to assess the performance of ML algorithms in predicting 30-day complications following TJA in racial and ethnic minority patients., Methods: A total of 267,194 patients undergoing primary TJA between 2013 and 2020 were identified from a national outcomes database. The patient cohort was stratified according to race, with further sub-stratification into Hispanic or non-Hispanic ethnicity. There were two ML algorithms, histogram-based gradient boosting (HGB), and random forest (RF), that were modeled to predict 30-day complications following primary TJA in the overall population. They were subsequently assessed in each racial and ethnic subcohort using discrimination, calibration, accuracy, and potential clinical usefulness., Results: Both models achieved excellent (Area under the curve (AUC) > 0.8) discrimination (AUC HGB = AUC RF = 0.86), calibration, and accuracy (HGB: slope = 1.00, intercept = -0.03, Brier score = 0.12; RF: slope = 0.97, intercept = 0.02, Brier score = 0.12) in the non-Hispanic White population (N = 224,073). Discrimination decreased in the White Hispanic (N = 10,429; AUC = 0.75 to 0.76), Black (N = 25,116; AUC = 0.77), Black Hispanic (N = 240; AUC = 0.78), Asian non-Hispanic (N = 4,809; AUC = 0.78 to 0.79), and overall (N = 267,194; AUC = 0.75 to 0.76) cohorts, but remained well-calibrated. We noted the poorest model discrimination (N = 1,870; AUC = 0.67 to 0.68) and calibration in the American-Indian cohort., Conclusion: The ML algorithms demonstrate an inferior predictive ability for 30-day complications following primary TJA in racial and ethnic minorities when trained on existing healthcare big data. This may be attributed to the disproportionate underrepresentation of minority groups within these databases, as demonstrated by the smaller sample sizes available to train the machine learning models. The ML models developed using smaller datasets (e.g., in racial and ethnic minorities) may not be as accurate as larger datasets, highlighting the need for equity-conscious model development., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Artificial intelligence (AI) has meant a turning point in data analysis, allowing predictions of unseen outcomes with precedented levels of accuracy. In multiple sclerosis (MS), a chronic inflammatory-demyelinating condition of the central nervous system with a complex pathogenesis and potentially devastating consequences, AI-based models have shown promising preliminary results, especially when using neuroimaging data as model input or predictor variables. The application of AI-based methodologies to serum/blood and CSF biomarkers has been less explored, according to the literature, despite its great potential. In this review, we aimed to investigate and summarise the recent advances in AI methods applied to body fluid biomarkers in MS, highlighting the key features of the most representative studies, while illustrating their limitations and future directions., Competing Interests: GA has received compensation for consulting services, speaking honoraria or participation in advisory boards from Merck, Roche, and Horizon Therapeutics; and travel support for scientific meetings from Novartis, Roche, ECTRIMS and EAN. She serves as editor for Europe of the Multiple Sclerosis Journal – Experimental, Translational and Clinical journal; and as a member of the editorial and scientific committee of Acta Neurológica Colombiana. She is a member of the International Women in Multiple Sclerosis iWiMS network executive committee, of the European Biomarkers in Multiple Sclerosis BioMS-eu steering committee, and of the MOGAD Eugene Devic European Network MEDEN steering group. MC has received compensation for consulting services and speaking honoraria from Bayer Schering Pharma, Merck Serono, Biogen-Idec, Teva Pharmaceuticals, Sanofi-Aventis, Genzyme, and Novartis. CT is currently being funded by a Miguel Servet contract, awarded by the Instituto de Salud Carlos III ISCIII, Ministerio de Ciencia e Innovación de España CP23/00117. She has also received a 2020 Junior Leader La Caixa Fellowship fellowship code: LCF/BQ/PI20/11760008, awarded by “la Caixa” Foundation ID 100010434, a 2021 Merck’s Award for the Investigation in MS, awarded by Fundación Merck Salud Spain, a 2021 Research Grant PI21/01860 awarded by the ISCIII, Ministerio de Ciencia e Innovación de España, and a FORTALECE research grant FORT23/00034 also by the ISCIII, Ministerio de Ciencia e Innovación de España. In 2015, she received an ECTRIMS Post-doctoral Research Fellowship and has received funding from the UK MS Society. She is a member of the Editorial Board of Neurology Journal and Multiple Sclerosis Journal. She has also received honoraria from Roche, Sanofi, Bristol-Myers Squibb, and Novartis and is a steering committee member of the O’HAND trial and of the Consensus group on Follow-on DMTs., (Copyright © 2024 Arrambide, Comabella and Tur.)

Medical oncology, through conventional chemotherapy as well as targeted drugs, remains an important component of cancer patient management, particularly for systemic disease. Despite advances in all areas of medical oncology, certain challenges persist in the form of drug resistance and severe normal tissue toxicity. These unwanted effects can be counteracted through a patient-tailored treatment approach, which in chemotherapy is translated as pharmacogenomics. This research field investigates the way genetic makeup influences a patient's response to various drugs with the aim to minimize trial-and-error associated with drug administration. The paper introduces the role, advances and challenges of pharmacogenomics, highlighting the importance of Big Data mining to reveal the mechanisms behind drug-gene pair interaction for better patient outcomes. International consortiums have prioritized their focus on the clinical implementation of pharmacogenomics while tackling the challenges ahead: data standardization, ethical aspects and the education of physicians and patients alike to comprehend the power of pharmacogenomics to transform medical oncology., Competing Interests: The authors declare that there is no conflict of interest., (© The Author(s), 2024.)